oru.sePublikasjoner
Endre søk
Begrens søket
1 - 5 of 5
RefereraExporteraLink til resultatlisten
Permanent link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Treff pr side
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sortering
  • Standard (Relevans)
  • Forfatter A-Ø
  • Forfatter Ø-A
  • Tittel A-Ø
  • Tittel Ø-A
  • Type publikasjon A-Ø
  • Type publikasjon Ø-A
  • Eldste først
  • Nyeste først
  • Skapad (Eldste først)
  • Skapad (Nyeste først)
  • Senast uppdaterad (Eldste først)
  • Senast uppdaterad (Nyeste først)
  • Disputationsdatum (tidligste først)
  • Disputationsdatum (siste først)
  • Standard (Relevans)
  • Forfatter A-Ø
  • Forfatter Ø-A
  • Tittel A-Ø
  • Tittel Ø-A
  • Type publikasjon A-Ø
  • Type publikasjon Ø-A
  • Eldste først
  • Nyeste først
  • Skapad (Eldste først)
  • Skapad (Nyeste først)
  • Senast uppdaterad (Eldste først)
  • Senast uppdaterad (Nyeste først)
  • Disputationsdatum (tidligste først)
  • Disputationsdatum (siste først)
Merk
Maxantalet träffar du kan exportera från sökgränssnittet är 250. Vid större uttag använd dig av utsökningar.
  • 1.
    Kalaykov, Ivan
    et al.
    Örebro universitet, Institutionen för teknik.
    Tolt, Gustav
    Örebro universitet, Institutionen för teknik.
    Fast fuzzy signal and image processing hardware2002Inngår i: Proceedings, NAFIPS 2002: Annual meeting of the North American fuzzy information processing society, 2002, 2002, s. 7-12Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The paper presents the development of fast fuzzy logic based hardware for various applications such as controllers for very fast processes, real-time image processing and pattern recognition. It is based on the fired-rules-hyper-cube (FRHC) concept, characterized by extremely simple way of the fuzzy inference in a layered parallel architecture. The processing time slightly depends on the number of inputs of the fuzzy system and does not depend on the number of rules and fuzzy partitioning of all variables. Most important is the inherent high speed of processing because of the parallelism and pipelining, implemented in all layers.

  • 2.
    Tolt, Gustav
    Örebro universitet, Institutionen för teknik.
    Fuzzy similarity-based image processing2005Doktoravhandling, monografi (Annet vitenskapelig)
    Abstract [en]

    Computer vision problems require low-level operations, e.g. noise reduction and edge detection, as well as high-level operations, e.g. object recognition and image understanding. Letting a PC carry out all computations is convenient but quite inefficient. One approach for improving the performance of the vision system is to bring as much as possible of the computationally intensive low-level operations closer to the camera using dedicated hardware devices, thus letting the PC focus on high-level tasks. In this thesis we present novel fuzzy techniques for reducing noise, determining edgeness and detecting junctions as well as stereo matching measures for color images, as building blocks of complex vision systems, e.g. for robot motion control or other industrial applications.

    The noise reduction is achieved by evaluating a number of fuzzy rules, each suggesting a particular filtering output. The firing strengths of the rules correspond to the degrees of similarity found among the pixels in the local processing window. The approach for determining edgeness is based on fuzzy rules that combine the estimated gradient magnitude with information about the homogeneity in different parts of the processing window. In this way the response from false edges is suppressed. In the junction detection approach we let the intersection between fuzzy sets represent the similarity between information obtained with different window sizes. The fuzzy sets represent the possible orientations of line segments in the window and non-zero intersections of the fuzzy sets indicate the presence of line segments in the window. The number of line segments characterize the nature of the junction. For the stereo matching measures, the global similarity betwen two pixels is defined in terms of fuzzy conjunctions of local similarities (color and edgeness). The proposed techniques have been designed for hardware implementation, making use of extensive parallelism and primarily simple numerical operations. The performance is shown in a number of experiments, and the strengths and limitations of the techniques are discussed.

  • 3.
    Tolt, Gustav
    Örebro universitet, Institutionen för teknik.
    Fuzzy-similarity-based low-level image processing: licentiate thesis2003Licentiatavhandling, monografi (Annet vitenskapelig)
  • 4.
    Tolt, Gustav
    et al.
    Örebro universitet, Institutionen för teknik.
    Kalaykov, Ivan
    Örebro universitet, Institutionen för teknik.
    A fuzzy-similarity-based approach for high-speed real-time image processing2005Inngår i: Intelligent Control, 2005 : Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation , 2005, s. 1240-1245Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this paper, we present a number of algorithms for performing some basic image processing tasks. The common denominator is the fuzzy similarity framework, that is used for representing vagueness and uncertainty associated with the similarity concept. The algorithms are designed so as to be implementable on FPGAs, making extensive use of the FPGA's parallel processing capabilities. Due to the limited space, we give pointers to previously published work for more details about the algorithms

  • 5.
    Tolt, Gustav
    et al.
    Örebro universitet, Institutionen för teknik.
    Kalaykov, Ivan
    Örebro universitet, Institutionen för teknik.
    Fuzzy-similarity-based noise cancellation for real-time image processing2001Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We introduce a new algorithm for image noise cancellation based on fuzzy similarity and homogeneity. The proposed method allows simple tuning of fuzzy filter properties and it is very convenient for high-speed real-time image processing. A detailed analysis of the filter properties is presented to support tuning its parameters for a particular application. Test examples and comparisons with other image noise cancellation techniques show the advantages of the method.

1 - 5 of 5
RefereraExporteraLink til resultatlisten
Permanent link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf